OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society. In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features. Our aim is to automatically construct a synthesised dataset by transforming facial images across varying racial domains, while still preserving identity-related features, such that racially dependant features subsequently become irrelevant within the determination of subject identity. We construct our experiments on three significant face recognition variants: Softmax, CosFace and ArcFace loss over a common convolutional neural network backbone. In a side-by-side comparison, we show the positive impact our proposed technique can have on the recognition performance for (racial) minority groups within an originally imbalanced training dataset by reducing the pre-race variance in performance.
ResNeSt: Split-Attention Networks
Venue: arXiv cs.CV 2020
Year: 2020

It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available.
ferify: A Virtual Machine File Protection System against Zero-Day Attacks
Venue: arXiv cs.CR 2020
Year: 2020

Most existing solutions for protecting VMs assume known attack patterns or signatures and focus on detecting malicious manipulations of system files and kernel level memory structures. In this research we develop a system called ferify, which leverages VM introspection (VMI) to protect user files hosted on a VM against unauthorized access even after an attacker has managed to obtain root privileges on the VM. ferify maintains in the hypervisor domain a shadow file access control list (SACL) that is totally transparent to the VM. It uses the SACL to perform independent access control on all system calls that may operate on the target files. Further, ferify prevents kernel modification, ensures the integrity of process ownership, and supports hypervisor based user authentication. We have developed a ferify prototype for Linux and through a set of controlled experiments we show that the system is able to mitigate a range of zero-day attacks that otherwise may evade signature-based solutions. In addition, we analyze the root cause of the observed high processing overhead from trapping of system calls, and propose a general solution that can potentially cut that overhead by half.
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
Venue: arXiv cs.NE 2020
Year: 2020

Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being proposed. Yet, solid comparisons with simple baselines are often missing. At the same time, recent retrospective studies have found many new algorithms to be no better than random search (RS). In this work we consider, for the first time, a simple Local Search (LS) algorithm for NAS. We particularly consider a multi-objective NAS formulation, with network accuracy and network complexity as two objectives, as understanding the trade-off between these two objectives is arguably the most interesting aspect of NAS. The proposed LS algorithm is compared with RS and two evolutionary algorithms (EAs), as these are often heralded as being ideal for multi-objective optimization. To promote reproducibility, we create and release two benchmark datasets, named MacroNAS-C10 and MacroNAS-C100, containing 200K saved network evaluations for two established image classification tasks, CIFAR-10 and CIFAR-100. Our benchmarks are designed to be complementary to existing benchmarks, especially in that they are better suited for multi-objective search. We additionally consider a version of the problem with a much larger architecture space. While we find and show that the considered algorithms explore the search space in fundamentally different ways, we also find that LS substantially outperforms RS and even performs nearly as good as state-of-the-art EAs. We believe that this provides strong evidence that LS is truly a competitive baseline for NAS against which new NAS algorithms should be benchmarked.
A Generalized Dimming Control Scheme for Visible Light Communications
Venue: arXiv cs.IT 2020
Year: 2020

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The proposed GDC scheme achieves dimming control by simultaneously adjusting the intensity of transmitted symbols and the number of active elements in a space-time matrix. Both the indices of the active elements in each space-time matrix and the modulated constellation symbols are used to carry information. Since illumination is deemed as the prior task of VLC, an incremental algorithm for index mapping is proposed for achieving target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to further improve the bit-error rate (BER) performance. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme with the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDCMFD) are proposed. The first GDC-MFD scheme, coined as GDC-MFD1, reduces the computational complexity by deriving a lower bound of the free distance based on Rayleigh-Ritz theorem. Based on the time-invariance characteristics of the VLC channel, GDC-MFD2 is proposed to further reduce the required computation efforts. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve 2 dB performance gains over conventional hybrid dimming control scheme and 7 dB performance gains over digital dimming control schemes.
Pose Manipulation with Identity Preservation
Venue: arXiv cs.CV 2020
Year: 2020

This paper describes a new model which generates images in novel poses e.g. by altering face expression and orientation, from just a few instances of a human subject. Unlike previous approaches which require large datasets of a specific person for training, our approach may start from a scarce set of images, even from a single image. To this end, we introduce Character Adaptive Identity Normalization GAN (CainGAN) which uses spatial characteristic features extracted by an embedder and combined across source images. The identity information is propagated throughout the network by applying conditional normalization. After extensive adversarial training, CainGAN receives figures of faces from a certain individual and produces new ones while preserving the person's identity. Experimental results show that the quality of generated images scales with the size of the input set used during inference. Furthermore, quantitative measurements indicate that CainGAN performs better compared to other methods when training data is limited.
Improving Resources Management in Network Virtualization by Utilizing a Software-Based Network
Venue: arXiv cs.NI 2020
Year: 2020

Network virtualization is a way to simultaneously run multiple heterogeneous architectures on a shared substrate. The main issue in network virtualization is mapping virtual networks to substrate network. How to manage substrate resources in mapping phase will have an effective role in improving the use of infrastructure resources. Using software-based networks in network virtualization which separates control logic from data as a new technology, has led to efficient resource management in this context. In this article a software-based network approach has been presented to network virtualization and manage infrastructure resources efficiently. It optimizes mapping function by dynamic resource management of infrastructure resource. We have added a module in the controller to manage the resources dynamically. An initial mapping will be done for arriving new requests based on number of successful requests and arriving time slots. They will not be finalized by writing the rules in the switches before arriving n requests. If some remapping during the n time window is needed, remapping can be done by the controller and the final results are sent to the switches to write the flow rules. The simulation has been done using NS2 simulator showed based on different evaluation criteria such as acceptance rate, average link utilization, cost and delay. Keywords: network virtualization, software based network, Virtual network mapping, substrate network, Virtual network.
Software Test Automation Maturity -- A Survey of the State of the Practice
Venue: arXiv cs.SE 2020
Year: 2020

The software industry has seen an increasing interest in test automation. In this paper, we present a test automation maturity survey serving as a self-assessment for practitioners. Based on responses of 151 practitioners coming from above 101 organizations in 25 countries, we make observations regarding the state of the practice of test automation maturity: a) The level of test automation maturity in different organizations is differentiated by the practices they adopt; b) Practitioner reported the quite diverse situation with respect to different practices, e.g., 85\% practitioners agreed that their test teams have enough test automation expertise and skills, while 47\% of practitioners admitted that there is lack of guidelines on designing and executing automated tests; c) Some practices are strongly correlated and/or closely clustered; d) The percentage of automated test cases and the use of Agile and/or DevOps development models are good indicators for a higher test automation maturity level; (e) The roles of practitioners may affect response variation, e.g., QA engineers give the most optimistic answers, consultants give the most pessimistic answers. Our results give an insight into present test automation processes and practices and indicate chances for further improvement in the present industry.
CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework -- c(C)la(a)ss increment(t)al net(Net)work (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset -- EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://github.com/villawang/CatNet.
Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Venue: arXiv cs.CV 2020
Year: 2020

Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to correct the susceptibility artefact, still present even in MAR-specific acquisitions. In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI. Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. Experiments show that our approach favours a smoother correction in the CT, while promoting signal recovery in the MRI.
On the Evaluation of Military Simulations: Towards A Taxonomy of Assessment Criteria
Venue: arXiv cs.CY 2020
Year: 2020

In the area of military simulations, a multitude of different approaches is available. Close Combat Tactical Trainer, Joint Tactical Combat Training System, Battle Force Tactical Training or Warfighter's Simulation 2000 are just some examples within the history of the large DoD Development Program in Modelling and Simulation, representing just a small piece of the variety of diverse solutions. Very often, individual simulators are very unique and so it is often difficult to classify military simulations even for experienced users. This circumstance is further boosted due to the fact that in the field of military simulations - unlike in other areas - no general classification for military simulations exists. To address this shortcoming, this publication is dedicated to the idea of providing a first contribution to the development of a commonly accepted taxonomy in the area of military simulations. To this end, the problem field is structured into three main categories (general functional requirements for simulators, special military requirements for simulators and non-functional requirements for simulators). Based upon that, individual categories are provided with appropriate classes. For a better understanding, the taxonomy is also applied to a concrete example (NetLogo Rebellion).
Deep Unfolded Multicast Beamforming
Venue: arXiv cs.IT 2020
Year: 2020

Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.
Complex-Object Visual Inspection via Multiple Lighting Configurations
Venue: arXiv cs.CV 2020
Year: 2020

The design of an automatic visual inspection system is usually performed in two stages. While the first stage consists in selecting the most suitable hardware setup for highlighting most effectively the defects on the surface to be inspected, the second stage concerns the development of algorithmic solutions to exploit the potentials offered by the collected data. In this paper, first, we present a novel illumination setup embedding four illumination configurations to resemble diffused, dark-field, and front lighting techniques. Second, we analyze the contributions brought by deploying the proposed setup in training phase only - mimicking the scenario in which an already developed visual inspection system cannot be modified on the customer site - and in evaluation phase. Along with an exhaustive set of experiments, in this paper, we demonstrate the suitability of the proposed setup for effective illumination of complex-objects, defined as manufactured items with variable surface characteristics that cannot be determined a priori. Moreover, we discuss the importance of multiple light configurations availability during training and their natural boosting effect which, without the need to modify the system design in evaluation phase, lead to improvements in the overall system performance.
Class Distribution Alignment for Adversarial Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not sufficiently consider the class distribution information between the two domains, which could adversely affect the reduction of domain gap. To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains. It integrates a discriminative structure-preserving loss and a joint adversarial generation loss. The former effectively prevents undesired label-flipping during the whole process of image translation, while the latter maintains the joint distribution alignment of images and labels. Furthermore, our approach enforces the classification consistence of target domain images before and after adaptation to aid the classifier training in both domains. Extensive experiments were conducted on multiple benchmark datasets including Digits, Faces, Scenes and Office31, showing that our approach achieved superior classification in the target domain when compared to the state-of-the-art methods. Also, both qualitative and quantitative results well supported our motivation that aligning the class distributions can indeed improve domain adaptation.
PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Code-mixing is the phenomenon of using more than one language in a sentence. It is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, it adds to the challenge of processing and understanding natural language to a much larger extent. This paper presents a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators. We are releasing the parallel corpus to facilitate future research opportunities in code-mixed machine translation. The annotated corpus is available at https://doi.org/10.5281/zenodo.3605597.
Secure Boot from Non-Volatile Memory for Programmable SoC Architectures
Venue: arXiv cs.CR 2020
Year: 2020

In modern embedded systems, the trust in comprehensive security standards all along the product life cycle has become an increasingly important access-to-market requirement. However, these security standards rely on mandatory immunity assumptions such as the integrity and authenticity of an initial system configuration typically loaded from Non-Volatile Memory (NVM). This applies especially to FPGA-based Programmable System-on-Chip (PSoC) architectures, since object codes as well as configuration data easily exceed the capacity of a secure bootROM. In this context, an attacker could try to alter the content of the NVM device in order to manipulate the system. The PSoC therefore relies on the integrity of the NVM particularly at boot-time. In this paper, we propose a methodology for securely booting from an NVM in a potentially unsecure environment by exploiting the reconfigurable logic of the FPGA. Here, the FPGA serves as a secure anchor point by performing required integrity and authenticity verifications prior to the configuration and execution of any user application loaded from the NVM on the PSoC. The proposed secure boot process is based on the following assumptions and steps: 1) The boot configurationis stored on a fully encrypted Secure Digital memory card (SD card) or alternatively Flash acting as NVM. 2) At boot time, a hardware design called Trusted Memory-Interface Unit (TMIU) is loaded to verify first the authenticity of the deployed NVM and then after decryption the integrity of its content. To demonstrate the practicability of our approach, we integrated the methodology into the vendor-specific secure boot process of a Xilinx Zynq PSoC and evaluated the design objectives performance, power and resource costs.
StereoSet: Measuring stereotypical bias in pretrained language models
Venue: arXiv cs.CL 2020
Year: 2020

A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or Asians are bad drivers. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real world data, they are known to capture stereotypical biases. In order to assess the adverse effects of these models, it is important to quantify the bias captured in them. Existing literature on quantifying bias evaluates pretrained language models on a small set of artificially constructed bias-assessing sentences. We present StereoSet, a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion. We evaluate popular models like BERT, GPT-2, RoBERTa, and XLNet on our dataset and show that these models exhibit strong stereotypical biases. We also present a leaderboard with a hidden test set to track the bias of future language models at https://stereoset.mit.edu